IDEAS home Printed from https://ideas.repec.org/p/vnm/wpaper/168.html
   My bibliography  Save this paper

Allocative efficiency and traders' protection under zero intelligence behavior

Author

Listed:
  • Marco LiCalzi

    (Department of Applied Mathematics, University of Venice)

  • Lucia Milone

    (Advanced School of Economics, University of Venice)

  • Paolo Pellizzari

    (Department of Applied Mathematics, University of Venice)

Abstract

This paper studies the continuous double auction from the point of view of market engineering: we tweak a resampling rule often used for this exchange protocol and search for an improved design. We assume zero intelligence trading as a lower bound for more robust behavioral rules and look at allocative efficiency, as well as three subordinate performance criteria: mean spread, cancellation rate, and traders' protection. This latter notion measures the ability of a protocol to help traders capture their share of the competitive equilibrium profits. We consider two families of resampling rules and obtain the following results. Full resampling is not necessary to attain high allocative efficiency, but fine-tuning the resampling rate is important. The best allocative performances are similar across the two families. However, if the market designer adds any of the other three criteria as a subordinate goal, then a resampling rule based on a price band around the best quotes is superior.

Suggested Citation

  • Marco LiCalzi & Lucia Milone & Paolo Pellizzari, 2008. "Allocative efficiency and traders' protection under zero intelligence behavior," Working Papers 168, Department of Applied Mathematics, Università Ca' Foscari Venezia, revised Nov 2009.
    • Handle: RePEc:vnm:wpaper:168
    as

    Download full text from publisher

    File URL: http://virgo.unive.it/wpideas/storage/2008wp168.pdf
    File Function: First version, 2008
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Marco LiCalzi & Paolo Pellizzari, 2008. "Zero-Intelligence Trading Without Resampling," Lecture Notes in Economics and Mathematical Systems, in: Klaus Schredelseker & Florian Hauser (ed.), Complexity and Artificial Markets, chapter 1, pages 3-14, Springer.
    2. Mirowski, Philip, 2007. "Markets come to bits: Evolution, computation and markomata in economic science," Journal of Economic Behavior & Organization, Elsevier, vol. 63(2), pages 209-242, June.
    3. Sunder, S., 1992. "Lower Bounds for Efficiency of Surplus Extraction in Double Auctions," GSIA Working Papers 1992-17, Carnegie Mellon University, Tepper School of Business.
    4. LiCalzi, Marco & Pellizzari, Paolo, 2007. "Simple market protocols for efficient risk sharing," Journal of Economic Dynamics and Control, Elsevier, vol. 31(11), pages 3568-3590, November.
    5. Gode, Dhananjay (Dan) K. & Sunder, Shyam, 2004. "Double auction dynamics: structural effects of non-binding price controls," Journal of Economic Dynamics and Control, Elsevier, vol. 28(9), pages 1707-1731, July.
    6. Marco LiCalzi & Paolo Pellizzari, 2006. "The Allocative Effectiveness of Market Protocols Under Intelligent Trading," Lecture Notes in Economics and Mathematical Systems, in: Charlotte Bruun (ed.), Advances in Artificial Economics, chapter 2, pages 17-29, Springer.
    7. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-137, February.
    8. Smith, Vernon L, 1982. "Microeconomic Systems as an Experimental Science," American Economic Review, American Economic Association, vol. 72(5), pages 923-955, December.
    9. Dhananjay K. Gode & Shyam Sunder, 1997. "What Makes Markets Allocationally Efficient?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(2), pages 603-630.
    10. Alvin E. Roth, 2002. "The Economist as Engineer: Game Theory, Experimentation, and Computation as Tools for Design Economics," Econometrica, Econometric Society, vol. 70(4), pages 1341-1378, July.
    11. Hurwicz, Leonid & Radner, Roy & Reiter, Stanley, 1975. "A Stochastic Decentralized Resource Allocation Process: Part I," Econometrica, Econometric Society, vol. 43(2), pages 187-221, March.
    12. Gul, Faruk & Lundholm, Russell, 1995. "Endogenous Timing and the Clustering of Agents' Decisions," Journal of Political Economy, University of Chicago Press, vol. 103(5), pages 1039-1066, October.
    13. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    14. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    15. Maslov, Sergei, 2000. "Simple model of a limit order-driven market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 278(3), pages 571-578.
    16. Steven Gjerstad & Jason M. Shachat, 2007. "Individual Rationality and Market Efficiency," Purdue University Economics Working Papers 1204, Purdue University, Department of Economics.
    17. Charlotte Bruun (ed.), 2006. "Advances in Artificial Economics," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-540-37249-3, October.
    18. Arifovic, Jasmina & Ledyard, John, 2007. "Call market book information and efficiency," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1971-2000, June.
    19. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    20. Crockett, Sean & Spear, Stephen & Sunder, Shyam, 2008. "Learning competitive equilibrium," Journal of Mathematical Economics, Elsevier, vol. 44(7-8), pages 651-671, July.
    21. Paul Brewer & Maria Huang & Brad Nelson & Charles Plott, 2002. "On the Behavioral Foundations of the Law of Supply and Demand: Human Convergence and Robot Randomness," Experimental Economics, Springer;Economic Science Association, vol. 5(3), pages 179-208, December.
    22. Zhan, Wenjie & Friedman, Daniel, 2007. "Markups in double auction markets," Journal of Economic Dynamics and Control, Elsevier, vol. 31(9), pages 2984-3005, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Roberto Cervone & Stefano Galavotti & Marco LiCalzi, 2009. "Symmetric Equilibria in Double Auctions with Markdown Buyers and Markup Sellers," Lecture Notes in Economics and Mathematical Systems, in: Cesáreo Hernández & Marta Posada & Adolfo López-Paredes (ed.), Artificial Economics, chapter 0, pages 81-92, Springer.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mikhail Anufriev & Jasmina Arifovic & John Ledyard & Valentyn Panchenko, 2013. "Efficiency of continuous double auctions under individual evolutionary learning with full or limited information," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 539-573, July.
    2. Ladley, Dan & Schenk-Hoppé, Klaus Reiner, 2009. "Do stylised facts of order book markets need strategic behaviour?," Journal of Economic Dynamics and Control, Elsevier, vol. 33(4), pages 817-831, April.
    3. Marco LiCalzi & Paolo Pellizzari, 2008. "Zero-Intelligence Trading Without Resampling," Lecture Notes in Economics and Mathematical Systems, in: Klaus Schredelseker & Florian Hauser (ed.), Complexity and Artificial Markets, chapter 1, pages 3-14, Springer.
    4. Michiel Leur & Mikhail Anufriev, 2018. "Timing under individual evolutionary learning in a continuous double auction," Journal of Evolutionary Economics, Springer, vol. 28(3), pages 609-631, August.
    5. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    6. Paul Brewer & Maria Huang & Brad Nelson & Charles Plott, 2002. "On the Behavioral Foundations of the Law of Supply and Demand: Human Convergence and Robot Randomness," Experimental Economics, Springer;Economic Science Association, vol. 5(3), pages 179-208, December.
    7. Anufriev, Mikhail & Panchenko, Valentyn, 2009. "Asset prices, traders' behavior and market design," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1073-1090, May.
    8. LiCalzi, Marco & Pellizzari, Paolo, 2007. "Simple market protocols for efficient risk sharing," Journal of Economic Dynamics and Control, Elsevier, vol. 31(11), pages 3568-3590, November.
    9. Annalisa Fabretti & Tommy Gärling & Stefano Herzel & Martin Holmen, 2017. "Convex incentives in financial markets: an agent-based analysis," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 375-395, November.
    10. Troy Tassier, 2013. "Handbook of Research on Complexity, by J. Barkley Rosser, Jr. and Edward Elgar," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 39(1), pages 132-133.
    11. Shyam Sunder & MODELS A, 2002. "Markets as Artifacts: Aggregate Efficiency from Zero-Intelligence Traders," Yale School of Management Working Papers ysm284, Yale School of Management, revised 01 Sep 2004.
    12. Antonio Doria, Francisco, 2011. "J.B. Rosser Jr. , Handbook of Research on Complexity, Edward Elgar, Cheltenham, UK--Northampton, MA, USA (2009) 436 + viii pp., index, ISBN 978 1 84542 089 5 (cased)," Journal of Economic Behavior & Organization, Elsevier, vol. 78(1-2), pages 196-204, April.
    13. Adrien Querbes, 2018. "Banned from the sharing economy: an agent-based model of a peer-to-peer marketplace for consumer goods and services," Journal of Evolutionary Economics, Springer, vol. 28(3), pages 633-665, August.
    14. Olivier Brandouy & Angelo Corelli & Iryna Veryzhenko & Roger Waldeck, 2012. "A re-examination of the “zero is enough” hypothesis in the emergence of financial stylized facts," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 7(2), pages 223-248, October.
    15. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    16. Micola, Augusto Ruperez & Bunn, Derek W., 2008. "Crossholdings, concentration and information in capacity-constrained sealed bid-offer auctions," Journal of Economic Behavior & Organization, Elsevier, vol. 66(3-4), pages 748-766, June.
    17. Ross M. Miller, 2012. "The Effect Of Boundary Conditions On Efficiency And Pricing In Double‐Auction Markets With Zero‐Intelligence Agents," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(3), pages 179-188, July.
    18. Anufriev, Mikhail & Arifovic, Jasmina & Ledyard, John & Panchenko, Valentyn, 2022. "The role of information in a continuous double auction: An experiment and learning model," Journal of Economic Dynamics and Control, Elsevier, vol. 141(C).
    19. Giuseppe Attanasi & Samuele Centorrino & Ivan Moscati, 2011. "Double Auction Equilibrium and Efficiency in a Classroom Experimental Search Market," LERNA Working Papers 11.03.337, LERNA, University of Toulouse.
    20. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.

    More about this item

    Keywords

    market engineering; trading protocols; competitive share; exchange market;
    All these keywords.

    JEL classification:

    • D51 - Microeconomics - - General Equilibrium and Disequilibrium - - - Exchange and Production Economies
    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vnm:wpaper:168. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Daria Arkhipova (email available below). General contact details of provider: https://edirc.repec.org/data/dmvenit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.